RESUMEN

Objective: Using functional magnetic resonance imaging (fMRI) and deep learning to discover the spatial pattern of brain function, or functional brain networks (FBNs) has been attracted many reseachers. Most existing works focus on static FBNs or dynamic functional connectivity among fixed spatial network nodes, but ignore the potential dynamic/time-varying characteristics of the spatial networks themselves. And most of works based on the assumption of linearity and independence, that oversimplify the relationship between blood-oxygen level dependence signal changes and the heterogeneity of neuronal activity within voxels.Approach: To overcome these problems, we proposed a novel spatial-wise attention (SA) based method called Spatial and Channel-wise Attention Autoencoder (SCAAE) to discover the dynamic FBNs without the assumptions of linearity or independence. The core idea of SCAAE is to apply the SA to generate FBNs directly, relying solely on the spatial information present in fMRI volumes. Specifically, we trained the SCAAE in a self-supervised manner, using the autoencoder to guide the SA to focus on the activation regions. Experimental results show that the SA can generate multiple meaningful FBNs at each fMRI time point, which spatial similarity are close to the FBNs derived by known classical methods, such as independent component analysis.Main results: To validate the generalization of the method, we evaluate the approach on HCP-rest, HCP-task and ADHD-200 dataset. The results demonstrate that SA mechanism can be used to discover time-varying FBNs, and the identified dynamic FBNs over time clearly show the process of time-varying spatial patterns fading in and out.Significance: Thus we provide a novel method to understand human brain better. Code is available athttps://github.com/WhatAboutMyStar/SCAAE.

Asunto(s)

RESUMEN

Functional brain networks (FBNs) are spatial patterns of brain function that play a critical role in understanding human brain function. There are many proposed methods for mapping the spatial patterns of brain function, however they oversimplify the underlying assumptions of brain function and have various limitations such as linearity and independence. Additionally, current methods fail to account for the dynamic nature of FBNs, which limits their effectiveness in accurately characterizing these networks. To address these limitations, we present a novel deep learning and spatial-wise attention based model called Spatial-Temporal Convolutional Attention (STCA) to accurately model dynamic FBNs. Specifically, we train STCA in a self-supervised manner by utilizing a Convolutional Autoencoder to guide the STCA module in assigning higher attention weights to regions of functional activity. To validate the reliability of the results, we evaluate our approach on the HCP-task motor behavior dataset, the experimental results demonstrate that the STCA derived FBNs have higher spatial similarity with the templates and that the spatial similarity between the templates and the FBNs derived by STCA fluctuates with the task design over time, suggesting that STCA can reflect the dynamic changes of brain function, providing a powerful tool to better understand human brain function. Code is available at https://github.com/SNNUBIAI/STCAE.

Asunto(s)

RESUMEN

Recently, deep learning models have achieved superior performance for mapping functional brain networks from functional magnetic resonance imaging (fMRI) data compared with traditional methods. However, due to the lack of sufficient data and the high dimensionality of brain volume, deep learning models of fMRI tend to suffer from overfitting. In addition, existing methods rarely studied fMRI data augmentation and its application. To address these issues, we developed a VAE-GAN framework that combined a VAE (variational auto-encoder) with a GAN (generative adversarial net) for functional brain network identification and fMRI augmentation. As a generative model, the VAE-GAN models the distribution of fMRI so that it enables the extraction of more generalized features, and thus relieve the overfitting issue. The VAE-GAN is easier to train on fMRI than a standard GAN since it uses latent variables from VAE to generate fake data rather than relying on random noise that is used in a GAN, and it can generate higher quality of fake data than VAE since the discriminator can promote the training of the generator. In other words, the VAE-GAN inherits the advantages of VAE and GAN and avoids their limitations in modeling of fMRI data. Extensive experiments on task fMRI datasets from HCP have proved the effectiveness and superiority of the proposed VAE-GAN framework for identifying both temporal features and functional brain networks compared with existing models, and the quality of fake data is higher than those from VAE and GAN. The results on resting state fMRI of Attention Deficit Hyperactivity Disorder (ADHD)-200 dataset further demonstrated that the fake data generated by the VAE-GAN can help improve the performance of brain network modeling and ADHD classification.

Asunto(s)

RESUMEN

BACKGROUND: It has been recently shown that deep learning models exhibited remarkable performance of representing functional Magnetic Resonance Imaging (fMRI) data for the understanding of brain functional activities. With hierarchical structure, deep learning models can infer hierarchical functional brain networks (FBN) from fMRI. However, the applications of the hierarchical FBNs have been rarely studied. METHODS: In this work, we proposed a hierarchical recurrent variational auto-encoder (HRVAE) to unsupervisedly model the fMRI data. The trained HRVAE encoder can predict hierarchical temporal features from its three hidden layers, and thus can be regarded as a hierarchical feature extractor. Then LASSO (least absolute shrinkage and selection operator) regression was applied to estimate the corresponding hierarchical FBNs. Based on the hierarchical FBNs from each subject, we constructed a novel classification framework for brain disorder identification and test it on the Autism Brain Imaging Data Exchange (ABIDE) dataset, a world-wide multi-site database of autism spectrum disorder (ASD). We analyzed the hierarchy organization of FBNs, and finally used the overlaps of hierarchical FBNs as features to differentiate ASD from typically developing controls (TDC). RESULTS: The experimental results on 871 subjects from ABIDE dataset showed that the HRVAE model can effectively derive hierarchical FBNs including many well-known resting state networks (RSN). Moreover, the classification result improved the state-of-the-art by achieving a very high accuracy of 82.1 %. CONCLUSIONS: This work presents a novel data-driven deep learning method using fMRI data for ASD identification, which could provide valuable reference for clinical diagnosis. The classification results suggest that the interactions of hierarchical FBNs have association with brain disorder, which promotes the understanding of FBN hierarchy and could be applied to other brain disorder analysis.

Asunto(s)

RESUMEN

In human society, the choice of transportation mode between two cities is largely influenced by the distance between the regions. Similarly, when neurons communicate with each other within the cerebral cortex, do they establish their connections based on their physical distance? In this study, we employed a data-driven approach to explore the relationships between fiber length and corresponding geodesic distance between the fiber's two endpoints on brain surface. Diffusion-MRI-derived fiber streamlines were used to represent extra-cortical axonal connections between neurons or cortical regions, while geodesic paths between cortical points were employed to simulate intra-cortical connections. The results demonstrated that the geodesic distance between two cortical regions connected by a fiber streamline was greater than the fiber length most of the time, indicating that cortical regions tend to choose the shortest path for connection; whether it be an intra-cortical or extra-cortical route, especially when intra-cortical routes within cortical regions are longer than potential extrinsic fiber routes, there is an increased probability to establish fiber routes to connect the both regions. These findings were validated in a group of human brains and may provide insights into the underlying mechanisms of neuronal growth, connection, and wiring.

Asunto(s)

RESUMEN

The investigation of functional brain networks (FBNs) using task-based functional magnetic resonance imaging (tfMRI) has gained significant attention in the field of neuroimaging. Despite the availability of several methods for constructing FBNs, including traditional methods like GLM and deep learning methods such as spatiotemporal self-attention mechanism (STAAE), these methods have design and training limitations. Specifically, they do not consider the intrinsic characteristics of fMRI data, such as the possibility that the same signal value at different time points could represent different brain states and meanings. Furthermore, they overlook prior knowledge, such as task designs, during training. This study aims to overcome these limitations and develop a more efficient model by drawing inspiration from techniques in the field of natural language processing (NLP). The proposed model, called the Multi-head Attention-based Masked Sequence Model (MAMSM), uses a multi-headed attention mechanism and mask training approach to learn different states corresponding to the same voxel values. Additionally, it combines cosine similarity and task design curves to construct a novel loss function. The MAMSM was applied to seven task state datasets from the Human Connectome Project (HCP) tfMRI dataset. Experimental results showed that the features acquired by the MAMSM model exhibit a Pearson correlation coefficient with the task design curves above 0.95 on average. Moreover, the model can extract more meaningful networks beyond the known task-related brain networks. The experimental results demonstrated that MAMSM has great potential in advancing the understanding of functional brain networks.

RESUMEN

Fiber tract segmentation is a prerequisite for tract-based statistical analysis. Brain fiber streamlines obtained by diffusion magnetic resonance imaging and tractography technology are usually difficult to be leveraged directly, thus need to be segmented into fiber tracts. Previous research mainly consists of two steps: defining and computing the similarity features of fiber streamlines, then adopting machine learning algorithms for fiber clustering or classification. Defining the similarity feature is the basic premise and determines its potential reliability and application. In this study, we adopt geometric features for fiber tract segmentation and develop a novel descriptor (FiberGeoMap) for the corresponding representation, which can effectively depict fiber streamlines' shapes and positions. FiberGeoMap can differentiate fiber tracts within the same subject, meanwhile preserving the shape and position consistency across subjects, thus can identify common fiber tracts across brains. We also proposed a Transformer-based encoder network called FiberGeoMap Learner, to perform segmentation based on the geometric features. Experimental results showed that the proposed method can differentiate the 103 various fiber tracts, which outperformed the existing methods in both the number of categories and segmentation accuracy. Furthermore, the proposed method identified some fiber tracts that were statistically different on fractional anisotropy (FA), mean diffusion (MD), and fiber number ration in autism.

Asunto(s)

RESUMEN

Pseudomonas aeruginosa (PA) is known as one kind of extracellular pathogens. However, more evidence showed that PA encounters the intracellular environment in different mammalian cell types. Little is known of innate immune factors modulating intracellular PA survival. In the present study, we proposed that interferon-ß (IFN-ß) is beneficial to the survival of PA in the cytoplasm of macrophages. Furthermore, we found that interleukin-1ß (IL-1ß) induced by PA suppresses IFN-ß response driven by the cGAS-STING-TBK1 pathway. Mechanistically, IL-1ß decreased the production of cyclic GMP-AMP (cGAMP) by activating AKT kinase. cGAMP is necessarily sufficient to stimulate the transcription of IFN-ß via the STING adaptor-TBK1 kinase-IRF3 transcription factor axis. Thus, our findings uncovered a novel module for PA intracellular survival involving IFN-ß production restricted by IL-1ß and provided a strong rationale for a potential clinical strategy against pulmonary PA infection patients. IMPORTANCE The link between innate immunity and intracellular Pseudomonas aeruginosa is unclear. Our studies illuminated the role of interferon-ß (IFN-ß) in remote intracellular PA infection. Furthermore, our experimental evidence also indicated that IL-1ß is a negative regulator of IFN-ß production and, in particular, P. aeruginosa infection. The inhibition of IFN-ß may be used as a potential therapeutic method against pulmonary PA infection.

Asunto(s)

RESUMEN

Recent advancement in natural language processing (NLP) and medical imaging empowers the wide applicability of deep learning models. These developments have increased not only data understanding, but also knowledge of state-of-the-art architectures and their real-world potentials. Medical imaging researchers have recognized the limitations of only targeting images, as well as the importance of integrating multimodal inputs into medical image analysis. The lack of comprehensive surveys of the current literature, however, impedes the progress of this domain. Existing research perspectives, as well as the architectures, tasks, datasets, and performance measures examined in the present literature, are reviewed in this work, and we also provide a brief description of possible future directions in the field, aiming to provide researchers and healthcare professionals with a detailed summary of existing academic research and to provide rational insights to facilitate future research.

Asunto(s)

RESUMEN

Airway microenvironment played an important role in the progression of chronic respiratory disease. Here we showed that standardized pondus hydrogenii (pH) of exhaled breath condensate (EBC) of bronchiectasis patients was significantly lower than that of controls and was significantly correlated with bronchiectasis severity index (BSI) scores and disease prognosis. EBC pH was lower in severe patients than that in mild and moderate patients. Besides, acidic microenvironment deteriorated Pseudomonas aeruginosa (P. aeruginosa) pulmonary infection in mice models. Mechanistically, acidic microenvironment increased P. aeruginosa outer membrane vesicles (PA_OMVs) released and boosted it induced the activation of interferon regulatory factor3 (IRF3)-interferonß (IFN-ß) signalling pathway, ultimately compromised the anti-bacteria immunity. Targeted knockout of IRF3 or type 1 interferon receptor (IFNAR1) alleviated lung damage and lethality of mice after P. aeruginosa infection that aggravated by acidic microenvironment. Together, these findings identified airway acidification impaired host resistance to P. aeruginosa infection by enhancing it induced the activation of IRF3-IFN-ß signalling pathway. Standardized EBC pH may be a useful biomarker of disease severity and a potential therapeutic target for the refractory P. aeruginosa infection. The study also provided one more reference parameter for drug selection and new drug discovery for bronchiectasis.

Asunto(s)

RESUMEN

BACKGROUND AND OBJECTIVE: To understand brain cognition and disorders, modeling the mapping between mind and brain has been of great interest to the neuroscience community. The key is the brain representation, including functional brain networks (FBN) and their corresponding temporal features. Recently, it has been proven that deep learning models have superb representation power on functional magnetic resonance imaging (fMRI) over traditional machine learning methods. However, due to the lack of high-quality data and labels, deep learning models tend to suffer from overfitting in the training process. METHODS: In this work, we applied a recurrent Wasserstein generative adversarial net (RWGAN) to learn brain representation from volumetric fMRI data. Generative adversarial net (GAN) is widely used in natural image generation and is able to capture the distribution of the input data, which enables the extraction of generalized features from fMRI and thus relieves the overfitting issue. The recurrent layers in RWGAN are designed to better model the local temporal features of the fMRI time series. The discriminator of RWGAN works as a deep feature extractor. With LASSO regression, the RWGAN model can decompose the fMRI data into temporal features and spatial features (FBNs). Furthermore, the generator of RWGAN can generate high-quality new data for fMRI augmentation. RESULTS: The experimental results on seven tasks from the HCP dataset showed that the RWGAN can learn meaningful and interpretable temporal features and FBNs, compared to HCP task designs and general linear model (GLM) derived networks. Besides, the results on different training datasets showed that the RWGAN performed better on small datasets than other deep learning models. Moreover, we used the generator of RWGAN to yield fake subjects. The result showed that the fake data can also be used to learn meaningful representation compared to those learned from real data. CONCLUSIONS: To our best knowledge, this work is among the earliest attempts of applying generative deep learning for modeling fMRI data. The proposed RWGAN offers a novel methodology for learning brain representation from fMRI, and it can generate high-quality fake data for the potential use of fMRI data augmentation.

Asunto(s)

RESUMEN

Objective. Recently, deep learning models have been successfully applied in functional magnetic resonance imaging (fMRI) modeling and associated applications. However, there still exist at least two challenges. Firstly, due to the lack of sufficient data, deep learning models tend to suffer from overfitting in the training process. Secondly, it is still challenging to model the temporal dynamics from fMRI, due to that the brain state is continuously changing over scan time. In addition, existing methods rarely studied and applied fMRI data augmentation.Approach. In this work, we construct a deep recurrent variational auto-encoder (DRVAE) that combined variational auto-encoder and recurrent neural network, aiming to address all of the above mentioned challenges. The encoder of DRVAE can extract more generalized temporal features from assumed Gaussian distribution of input data, and the decoder of DRVAE can generate new data to increase training samples and thus partially relieve the overfitting issue. The recurrent layers in DRVAE are designed to effectively model the temporal dynamics of functional brain activities. LASSO (least absolute shrinkage and selection operator) regression is applied on the temporal features and input fMRI data to estimate the corresponding spatial networks.Main results. Extensive experimental results on seven tasks from HCP dataset showed that the DRVAE and LASSO framework can learn meaningful temporal patterns and spatial networks from both real data and generated data. The results on group-wise data and single subject suggest that the brain activities may follow certain distribution. Moreover, we applied DRVAE on four resting state fMRI datasets from ADHD-200 for data augmentation, and the results showed that the classification performances on augmented datasets have been considerably improved.Significance. The proposed method can not only derive meaningful temporal features and spatial networks from fMRI, but also generate high-quality new data for fMRI data augmentation and associated applications.

Asunto(s)

RESUMEN

Pseudomonas aeruginosa (PA) has emerged as a pressing challenge to pulmonary infection and lung damage. The LL37 peptide is an efficient antimicrobial agent against PA strains, but its application is limited because of fast clearance in vivo, biosafety concerns, and low bioavailability. Thus, an albumin-based nanodrug delivery system with reduction sensitivity was developed by forming intermolecular disulfide bonds to increase in vivo LL37 performance against PA. Cationic LL37 can be efficiently encapsulated via electrostatic interactions to exert improved antimicrobial effects. The LL37 peptide exhibits greater than 48 h of sustained released from LL37 peptide nanoparticles (LL37 PNP), and prolonged antimicrobial effects were noted as the incubation time increased. Levels of inflammatory cytokines secreted by peritoneal macrophages, including TNF-α and IL-6, were reduced significantly after LL37 PNP treatment following PA stimulation, indicating that LL37 PNP inhibits PA growth and exerts anti-inflammatory effects in vitro. In a murine model of acute PA lung infection, LL37 PNP significantly reduced TNF-α and IL-1ß expression and alleviated lung damage. The accelerated clearance of PA indicates that LL37 PNP could improve PA lung infection and the subsequent inflammation response more efficiently compared with free LL37 peptide. In conclusion, this excellent biocompatible LL37 delivery strategy may serve as an alternative approach for the application of new types of clinical treatment in future.

Asunto(s)

RESUMEN

Sesarmops sinensis is a dominant omnivorous crab species, which plays an important ecological function in salt marsh ecosystems. To better understand its immune system and immune related genes under pathogen infection, the transcriptome was analyzed by comparing the data of S. sinensis hepatopancreas stimulated by PBS and PGN. A set of assembly and annotation identified 39,039 unigenes with an average length of 1105 bp, obtaining 1300 differentially expressed genes (DEGs) in all, which included 466 remarkably up-regulated unigenes and 834 remarkably down-regulated unigenes. In addition, based on mensurable real time-polymerase chain reaction and high-throughput sequencing, several immune responsive genes were found to be markedly up-regulated under PGN stimulation. In conclusion, in addition to enriching the existing transcriptome data of S. sinensis, this study also clarified the immune response of S. sinensis to PGN stimulation, which will help us to further understand the crustacean's immune system.

Asunto(s)

RESUMEN

The role of corticosteroids in acute lung injury (ALI) remains uncertain. This study aims to determine the underlying mechanisms of corticosteroid treatment for lipopolysaccharide (LPS)-induced inflammation and ALI. We used corticosteroid treatment for LPS-induced murine ALI model to investigate the effect of corticosteroid on ALI in vivo. Moreover, LPS-stimulated macrophages were used to explore the specific anti-inflammatory effects of corticosteroids on NLRP3-inflammasome in vitro. We found corticosteroids attenuated LPS-induced ALI, which manifested in reduction of the alveolar structure destruction, the infiltration of neutrophils and the inflammatory cytokines release of interleukin-1ß (IL-1ß) and interleukin-18 (IL-18) in Lung. In vitro, when NLRP3-inflammasome was knocked out, inflammatory response of caspase-1 activation and IL-1ß secretion was obviously declined. Further exploration, our results showed that when corticosteroid preprocessed macrophages before LPS primed, it obviously inhibited the activation of caspase-1 and the maturation of IL-1ß, which depended on inhibiting the nuclear factor-κB (NF-κB) signal pathway activation. However, when corticosteroids intervened the LPS-primed macrophages, it also negatively regulated NLRP3-inflammasome activation through suppressing mitochondrial reactive oxygen species (mtROS) production. Our results revealed that corticosteroids played a protection role in LPS-induced inflammation and ALI by suppressing both NF-κB signal pathway and mtROS-dependent NLRP3 inflammasome activation.

Asunto(s)

RESUMEN

BACKGROUND: Emergency department (ED) overcrowding is a severe health care concern, while anxiety and depression rates among ED patients have been reported to be substantially higher compared to the general population. We hypothesized that anxiety due to over crowdedness may lead to adverse events in EDs. AIM: To investigate correlations between crowdedness in EDs and anxiety of patients and nurses, and to identify factors affecting their anxiety. METHODS: In this prospective observational study, a total 43 nurses and 389 emergency patients from two tier III hospitals located in Beijing were included from January 2016 to August 2017. Patients were grouped into inpatients when they were hospitalized after diagnoses, or into outpatients when they were discharged after treatments. The State Trait Anxiety Inventory (STAI Form Y) questionnaire was used to investigate patient and nurse anxieties, while crowdedness of EDs was evaluated with the National Emergency Department Over Crowding Score. RESULTS: The present results revealed that state anxiety scores (49.50 ± 6.00 vs 50.80 ± 2.80, P = 0.005) and trait anxiety scores (45.40 ± 5.70 vs 46.80 ± 2.70, P = 0.002) between inpatients (n = 173) and outpatients (n = 216) were significantly different, while the state anxiety of nurses (44.70 ± 5.80) was different from those of both patient groups. Generalized linear regression analysis demonstrated that multiple factors, including crowdedness in the ED, were associated with state and trait anxieties for both inpatients and outpatients. In addition, there was an interaction between state anxiety and trait anxieties. However, multivariable regression analysis showed that while overcrowding in the ED did not directly correlate with patients' and nurses' anxiety levels, the factors that did correlate with state and trait anxieties of inpatients were related to crowdedness. These factors included waiting time in the ED, the number of patients treated, and the number of nurses in the ED, whereas for nurses, only state and trait anxieties correlated significantly with each other. CONCLUSION: Waiting time, the number of patients treated, and the number of nurses present in the ED correlate with patient anxiety in EDs, but crowdedness has no effect on nurse or patient anxiety.

RESUMEN

PM2.5, a major component of air pollutants, has caused severe health problems. It has been reported that PM2.5 index is closely associated with severity of influenza A virus (IAV) infection. However, the underlying mechanisms have not been addressed. NLRP3 inflammasome and type I interferon signaling regulate host defense against influenza infection. The present study investigated the potential effects of air pollutants on host defense against influenza infection in vitro and in vivo. In this study, different concentrations of PM2.5 were pre-exposed to macrophages and mice before IAV infection to assess the negative effects of air pollutants in virus infection. We found that exposure to PM2.5 deteriorated influenza virus infection via compromising innate immune responses manifested by a decrease IL-1ß and IFN-ß production in vitro. Meanwhile, mice exposed with PM2.5 were susceptible to PR8 virus infection due to down-regulation of IL-1ß and IFN-ß. Mechanistically, PM 2.5 exposure suppressed the NLRP3 inflammasome activation and the AHR-TIPARP signaling pathway, by which compromised the anti-influenza immunity. Thus, our study revealed that PM2.5 could alter macrophage inflammatory responses by suppressing LPS-induced activation of NLRP3 inflammasome and expression of IFN-ß during influenza infection. These findings provided us new insights in understanding that PM2.5 combining with influenza infection could enhance the severity of pneumonia.

Asunto(s)

RESUMEN

It has been shown that deep neural networks are powerful and flexible models that can be applied on fMRI data with superb representation ability over traditional methods. However, a challenge of neural network architecture design has also attracted attention: due to the high dimension of fMRI volume images, the manual process of network model design is very time-consuming and not optimal. To tackle this problem, we proposed an unsupervised neural architecture search (NAS) framework on a deep belief network (DBN) that models volumetric fMRI data, named NAS-DBN. The NAS-DBN framework is based on Particle Swarm Optimization (PSO) where the swarms of neural architectures can evolve and converge to a feasible optimal solution. The experiments showed that the proposed NAS-DBN framework can quickly find a robust architecture of DBN, yielding a hierarchy organization of functional brain networks (FBNs) and temporal responses. Compared with 3 manually designed DBNs, the proposed NAS-DBN has the lowest testing loss of 0.0197, suggesting an overall performance improvement of up to 47.9 %. For each task, the NAS-DBN identified 260 FBNs, including task-specific FBNs and resting state networks (RSN), which have high overlap rates to general linear model (GLM) derived templates and independent component analysis (ICA) derived RSN templates. The average overlap rate of NAS-DBN to GLM on 20 task-specific FBNs is as high as 0.536, indicating a performance improvement of up to 63.9 % in respect of network modeling. Besides, we showed that the NAS-DBN can simultaneously generate temporal responses that resemble the task designs very well, and it was observed that widespread overlaps between FBNs from different layers of NAS-DBN model form a hierarchical organization of FBNs. Our NAS-DBN framework contributes an effective, unsupervised NAS method for modeling volumetric task fMRI data.

Asunto(s)

RESUMEN

Many existing studies for the mapping of function brain networks impose an implicit assumption that the networks' spatial distributions are constant over time. However, the latest research reports reveal that functional brain networks are dynamical and have time-varying spatial patterns. Furthermore, how these functional networks evolve over time has not been elaborated and explained in sufficient details yet. In this paper, we aim to discover and characterize the dynamics of functional brain networks via a windowed group-wise dictionary learning and sparse coding approach. First, we aggregated the sampled subjects' fMRI signals into one big data matrix, and learned a common dictionary for all individuals via a group-wise dictionary learning step. Second, we obtained the dynamic time-varying functional networks by using the windowed time-varying sparse coding approach. Experimental results demonstrated that our windowed group-wise dictionary learning and sparse coding method can effectively detect the task-evoked networks and also characterize how these networks evolve over time. This work sheds novel insights on the dynamics mechanism of functional brain networks.

Asunto(s)

RESUMEN

In this study, we identified a fish-specific Toll-like receptor (TLR) in Pelteobagrus fulvidraco, an economically important freshwater fish in China. This TLR, PfTLR26, was shown to be encoded by a 3084 bp open reading frame (ORF), producing a polypeptide 1027 amino acids in length. The PfTLR26 protein contains a signal peptide, eight leucine-rich repeat (LRR) domains, two LRR_TYP domains in the extracellular region, and a Toll/interleukin (IL)-1 receptor (TIR) domain in the cytoplasmic region, consistent with the characteristic TLR domain architecture. This predicted 117.1 kDa protein was highly homologous to those of other fish, with phylogenetic analysis revealing the closest relation to TLR26 of Ictalurus punctatus. Real-time quantitative reverse transcription-PCR (qRT-PCR) analysis showed that the PfTLR26 gene was expressed in all tissues tested, with the highest expression levels seen in the head kidney and blood, and the lowest seen in muscle. PfTLR26 exhibited significant upregulation in liver, spleen, head kidney, and blood at different time points following challenge with the common TLR agonists lipopolysaccharide (LPS) and polyriboinosinic polyribocytidylic acid (Poly I:C). Taken together, these results suggest that PfTLR26 may be an important component of the P. fulvidraco innate immune system, participating in the transduction of TLR signaling under pathogen stimulation.

Asunto(s)